Unlocking the Power of LLM with Retrieval Augmented Generation (RAG) ‍

Artificial intelligence has come a long way, and large language models (LLMs) are proving to be powerful tools but they have little knowledge of your business's data or processes so they are limited as are, in their functionality. To bridge the gap from a LLM, like ChatGPT or Palm, to produce value adding AI tools, you often need retrieval augmented generation (RAG) to ingest the models with your data.

At Kortical we work with large companies like Deloitte, NHS and HS2, providing them with tailor-made AI solutions that transform their business operations. In line with the latest innovations, our focus is on enhancing LLMs with RAG to create more advanced AI agents. These enhanced agents are capable of executing increasingly complex tasks, leading to greater automation and efficiency in various business processes.

What is Retrieval Augmented Generation?

RAG combines the natural language generation capabilities of LLMs with targeted information retrieval. It works by first searching an external knowledge source or database to find relevant information to the user's query or request. This retrieved content is then used to augment the original input, providing critical context and knowledge to inform the LLM's response.

For example, in an application of the RAG process when a customer asks a chatbot a question about an insurance policy, the system first searches the company's policy database to retrieve specific details about that customer's coverage. Instead of simply appending, the RAG system intelligently integrates this retrieved data with the original question. This integration is done in a way that maintains the context and relevance of both the query and the retrieved information. The result is a richly informed prompt that enables the LLM to generate a response that is not only accurate but also tailored to the specific nuances of the customer’s request.

Let's look at the video of KorticalChat’s RAG-powered insurance chatbot. It seamlessly integrated detailed policy information to deliver precise and helpful responses to a customer's insurance-related questions.

What problem do they solve?          

Bridge Knowledge Gap for Information Accuracy

Generative AI chatbots are excellent conversationalists but they rely on fixed datasets that can quickly become outdated. RAG innovates by integrating current, relevant data, which is especially beneficial for dynamic applications like interactive chatbots.

With RAG, AI can use the latest news or data to give better, more current answers. This is really helpful for services like online helpdesks, where getting the right info quickly matters a lot.

Ensuring Contextual Relevance

Standard generative AIs can struggle with providing information that is both accurate and contextually tailored. RAG enhances these systems by incorporating real-time data, which ensures that the AI's output is not only factual but also aligned with the latest developments.

For example, in sports analytics, RAG enables the AI to provide the latest statistics and updates on players and games, sourced directly from current databases and news outlets, keeping fans and professionals informed with the most recent and relevant data.

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Example of a RAG Workflow

Internal Team Knowledge Base and Sharing 

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The Challenges experienced from having to manually search:

• Information Retrieval: Historically labour-intensive and time-consuming to go to different sharepoint folders, databases and websites.
• Limited Access: Only a select few know the locations exist and can manipulate the extensive data and extract it in a meaningful way from the central database. 
• Document Location: Documents are hard to locate without knowing their existence or location.
• Time: Can take hours to search manually and often it is not possible to be exhaustive.

What the AI Chatbot solves for:

• Efficiency: Rapidly searches through thousands of records, bypassing complex filters
• Intuitiveness: No prior knowledge required, broad search capabilities.
• Presentation: Compiles information into an easily digestible format.
• Speed: Quick searches, facilitates easy and instant information sharing.
• Centralisation: A one-stop-shop for information retrieval, enhancing productivity.

Impact:

• Efficient Learning and sharing of knowledge: Facilitates quick and effective dissemination of information among team members, enhancing collective knowledge.
• Higher productivity and performance: Focus on high-value tasks by reducing time spent searching for information. Enables informed decision-making, thereby enhancing overall business outcomes.

Customer Service Enhancement

The Envestors Visa Application Assistant has been instrumental in supporting the team in managing the significant increase in application queries. This need arose particularly due to the endorsement body decreasing dramatically from 60 companies to just 3. 

The AI assistant offers a personalised experience, guiding users through web resources, and providing clear, concise answers to questions about visa application processes and other common inquiries, thereby streamlining the user experience and alleviating the workload on support staff.

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Challenge:

• High Labour Costs and Query Volume: The dual challenge of managing a high volume of customer queries while incurring significant labour costs to maintain a customer service team.

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What the AI Chatbot solves for:

• Automated Response Handling: Efficiently manages and responds to high volumes of queries, reducing the burden on human agents.
• Personalised Customer Interactions: Uses customer data to provide tailored responses, enhancing the relevance and quality of communication. 
• Cost-Effective Scaling: Provides a scalable solution that manages fluctuating query volumes without proportionally increasing labour costs.

Impact:

• Improved Customer Satisfaction: Faster and more accurate responses lead to higher customer satisfaction and reduced frustration.
• Increased Operational Efficiency: Frees up human agents to handle more complex queries. 
• Reduced Labour Expenses: Decreases overall operational costs with less reliance on human agents to offer 24/7 support.

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Steps for implementing a RAG system

If you were to implement a RAG system, here are the key steps for implementing it: 

1. Preparation - Knowledge Base Creation:

To lay the foundation for a robust RAG system, it is essential to prepare a knowledge base by ingesting data from the relevant sources, including applications, documents, and databases. The data should be formatted to ensure efficient searchability. This means transforming the raw data into a unified 'Document' object representation that can be easily indexed and retrieved.

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2. Ingestion Process - Vector Database Setup:

The ingestion process revolves around the utilisation of Vector Databases as knowledge bases. These databases use indexing algorithms to organise high-dimensional vectors, providing fast and robust querying abilities.

• Data Extraction: Begin by extracting data from the sourced documents.
• Data Chunking: Break down the documents into manageable chunks of data sections.
• Data Embedding: Convert these chunks into vector embeddings using models 
• User Query Ingestion: Develop a user-friendly mechanism, such as a UI or an API, to ingest natural language user queries and convert them into vector embeddings.

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3. Retrieval Process:

This phase involves matching user queries with the most relevant data chunks stored in the Vector Database.

• Query Embedding: Embed the user query data to align with the database's vector space.
• Chunk Retrieval: Conduct a hybrid search to retrieve the most relevant data chunks based on the query's vector embedding.
• Content Pulling: Extract the pertinent content from the knowledge base to provide context for the prompt.

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4. Generation Process:

The generation process entails creating prompts that combine the retrieved information with the original user query.

• Prompt Generation: Combine information with the query to form a prompt 
• Response Generation: Use a Large Language Model (LLM) like GPT-3 to generate informed responses based on the combined prompt.‍
• Task Execution: Instruct the LLM to perform specific tasks related to the query, such as display order tracking or fetching data from your back-end systems.

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5. Optimisation:

Finally, tailor and refine the RAG system to meet specific requirements and improve performance.

• Customisation: Adjust the ingestion flow, chunking parameters, and embedding models to fit the unique needs of your application.
• Optimisation: Implement strategies to enhance the quality of retrieval and reduce token counts, which can lead to performance improvements and cost savings at scale.

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Implementing RAG internally vs using ML platforms

Key challenges with implementing a RAG system entirely by yourself instead of leveraging an ML platform:

1. Requirements Analysis & Design Complexity: Determining all the software components and infrastructure needed for ingestion, knowledge encoding, retrieval, prompting and response generation requires significant upfront analysis and systems design expertise.

2. Algorithm Research & Testing: Extensive research and experimentation is needed to select the optimal algorithms for embeddings, similarity scoring, approximate nearest neighbour search, language modelling etc. This requires substantial ML ops experience.

3. Infrastructure Overhead: Procuring, configuring and maintaining all the infrastructure like storage, servers, load balancers, databases etc. carries high capital and operational expenses.

4. Model Development & Tuning: Developing customised vector encoding models optimised for your data as well as fine-tuning large pre-trained language models is resource intensive demanding high-powered GPU clusters.

5. Production Grade Platform: Addressing scalability, security, monitoring, redundancy for mission-critical production rollouts brings added requirements beyond just getting an MVP working.

6. Team Skill Sets: All the specialised skills like data engineering, ML engineering, backend dev, infra dev ops and security engineering needed are unlikely to exist within small internal teams.

By leveraging a purpose-built ML platform like KorticalChat, many of these complex challenges can be simplified so you only need to focus on the end-use case implementation and benefit realisation. The platform handles the heavy lifting of building and optimising the algorithms, models, data and infrastructure.

With KorticalChat, you can easily create your RAG powered AI chatbot in 2 minutes. 

Implement RAG workflow with KorticalChat

KorticalChat is an innovative platform designed to build AI Agents – sophisticated digital workers adept at performing specific tasks. These AI Agents are more than just chatbots; they're intelligent entities that understand the nuances of their assigned jobs and are integrated with the necessary tools to execute these tasks proficiently.

Core Capabilities of KorticalChat's AI Agents:

Data Capture: KorticalChat’s AI Agents can extract essential details from chats. They can intelligently gather and process data from conversations, ensuring that the relevant details are collected for accurate and effective task execution.

Process Adherence: These AI Agents can be programmed to follow designated processes meticulously. Whether it's guiding a customer through a troubleshooting process or managing a workflow, they ensure consistent, reliable adherence to set procedures.

Tool Integration: KorticalChat can be connected to an array of digital tools such as email systems, databases, and CRM platforms, enabling them to perform a wide range of functions seamlessly. This integration allows for more sophisticated interactions and solutions that go beyond simple question-and-answer formats.

Specialised Knowledge: Tailored to your specific business needs, they can provide expert assistance, advice, or guidance in various domains, making them a valuable asset in any industry.

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KorticalChat represents a new era of digital interaction, where AI-driven conversations drive business efficiency, growth, and customer satisfaction.

If you’re interested in how a RAG KorticalChat can elevate your business, contact us to discuss your specific needs and learn how we can tailor our AI solutions for you. Our team is ready to help you unlock the potential of AI, enhancing your processes and customer experiences.